1. Field
The present invention relates to the field of image processing, and in particular to an image denoising method and an image denoising apparatus.
2. Description of the Related Art
As the development and popularity of digital cameras and scanners, it is very easy to obtain digital images. However, any sensor in the real world is subjected to noise to a certain extent, such as heat, electric power, or other substances. Noise may damage real measurement of a signal, resulting in that any image data include signals and noise. Various applications related to images, such as medical image analysis, image segmentation, and subject detection, etc., basically need use of an effective noise suppression method to further generate reliable results. Hence, in image processing and computer visualization, image filtering has become an important and wide studies technology. A gray scale image is an important and commonly-used image, and its noise filtering method is extremely important.
Currently, there exist some denoising methods to attempt to filter noise of a gray scale image, such as a wavelet analysis method based on a threshold, a non-local mean value method, a Gaussian filtering method, and a bilateral filtering method.
In the wavelet analysis method based on a threshold, wavelet transform is applied to an original image, and the original image is converted into the wavelet domain, and a threshold method is used to filter multiband wavelet coefficients, which are usually a diagonal detail coefficient, a horizontal detail coefficient and a vertical detail coefficient, in a first decomposed stage. Wherein, a threshold value method is understood as a hard-thresholding function, all the detail coefficients less than the threshold value are set to be 0, and the remaining detail coefficients are reserved. Finally, after processing by using the threshold value method, all the wavelet coefficients are returned back to the image domain through wavelet transform. The wavelet analysis method based on a threshold may suppress noise, but at the same time, some details in the image are also suppressed.
The non-local mean value method is a nonlinear edge protection filtering method, in which each output pixel is calculated as a weighted sum of input pixels. A group of input pixels contributing to an output pixel are from a large area in an input image, hence it is referred to as non-localization. A key feature in a local mean value method is that a weight is determined according to a distance between small image blocks. This method is able to reserve details in an image and suppress high-frequency Gaussian noise. However, such a filtering method is not applicable to a real scenario where noise is severe and higher than the Gaussian noise.
The Gaussian filtering method is a weighted mean method. Each output pixel is set as a weight mean value of neighboring pixels of the pixel, a luminance value of an original pixel obtains a maximum weight, and the neighboring pixels obtain a relatively small weight according to increase of distance between them and the original pixel. After image filtering, noise is decreased, but at the same time, details in the images are also decreased.
The bilateral filtering method is an edge protection method and a denoising filtering method. A luminance value of each pixel in an image is replaced with a weight mean value of luminance values of its adjacent pixels. This weight is based on Gaussian distribution. What is crucial is that the weight depends not only on a Euclidean distance, but also on a radiometric difference. Clear edges may be reserved by systematically traversing each pixel and granting weights to corresponding neighboring pixels. If an image is serious affected by noise, this method makes the edges looked false and unnatural.